Article
Computer Science, Artificial Intelligence
Yongping Du, Lulin Wang, Zhi Peng, Wenyang Guo
Summary: In e-commerce platform, users' purchase behavior and reviews contain valuable information for recommendation. The proposed HACN model achieves better results in experiments across different domains from Amazon.
Article
Computer Science, Artificial Intelligence
Niloofar Ranjbar, Saeedeh Momtazi, MohammadMehdi Homayoonpour
Summary: Currently, there is extensive research in the field of artificial intelligence aiming to improve the explainability of deep learning models. By utilizing counterfactual reasoning, the minimal features can be altered to generate explainable outputs. This paper presents a method for generating explainable outputs and demonstrates its effectiveness on real-world datasets.
Article
Computer Science, Artificial Intelligence
Yun Liu, Jun Miyazaki
Summary: This paper proposes a knowledge-aware attentional neural network (KANN) for movie recommendation tasks. By extracting knowledge entities from movie reviews and capturing understandable interactions between users and movies, the proposed KANN can learn and understand users' explicit preferences for movies, achieving outstanding prediction performances in cases with a very small amount of reviews. The inclusion of knowledge graph representation enables KANN to provide high explainability in movie recommendations.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Ruxia Liang, Qian Zhang, Jianqiang Wang, Jie Lu
Summary: This article discusses the issue of data sparsity in group recommender systems and the role of cross-domain recommender systems in addressing the problem. A hierarchical attention network-based cross-domain group recommendation method called HAN-CDGR is proposed to provide accurate recommendation services for both groups and individual users.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Information Systems
Siyuan Guo, Ying Wang, Hao Yuan, Zeyu Huang, Jianwei Chen, Xin Wang
Summary: This study introduces a novel Triple-Attentional Explainable Recommendation method that jointly generates recommendation results and explanations, and demonstrates its effectiveness in both recommendation and explanation through comprehensive experiments on six real-world datasets.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Juan Ni, Zhenhua Huang, Jiujun Cheng, Shangce Gao
Summary: This paper proposes a novel recommendation model called RM-DRL, which consists of two modules: Information Preprocessing and Feature Representation. The Feature Representation module utilizes deep neural networks to learn semantic feature vectors of users and items, addressing the issue of current models failing to capture deep semantic features effectively.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Tianjun Wei, Tommy W. S. Chow, Jianghong Ma, Mingbo Zhao
Summary: The researchers proposed an Explanation-aware Graph Convolution Network (ExpGCN) that can generate node representations for both explanation ranking task and item recommendation task, improving the effectiveness of explainable recommendations.
Article
Business
Xiao Sha, Zhu Sun, Jie Zhang
Summary: The paper introduces a new hierarchical attentive knowledge graph embedding framework for effective recommendation. It extracts expressive subgraphs and encodes them to generate effective subgraph embeddings for enhanced user preference prediction. Extensive experiments show the superiority of the framework against state-of-the-art recommendation methods.
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Jiaying Chen, Jiong Yu, Wenjie Lu, Yurong Qian, Ping Li
Summary: This study introduces an interactive rules-guided recommender framework based on knowledge graph, enriching the content of knowledge graph and extracting paths and behavior rules to achieve better recommendation results.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Yuzhi Song, Hailiang Ye, Ming Li, Feilong Cao
Summary: This paper introduces a novel deep GNN model MAF-GNN, which improves the quality of latent feature representations for users and items through multi-graph attention fusion and multi-scale latent feature modules. Experimental results show that MAF-GNN outperforms existing methods in recommendation systems.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Computer Science, Artificial Intelligence
Yong Liu, Susen Yang, Yinan Zhang, Chunyan Miao, Zaiqing Nie, Juyong Zhang
Summary: This paper proposes a novel review-based recommendation model called Review Graph Neural Network (RGNN), which builds a specific review graph for each individual user/item to provide a global view and weaken biases caused by noise review information.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Yang Li, Tong Chen, Zi Huang
Summary: The AFRec recommender system tackles the explainability challenge in fashion recommendation tasks by explicitly leveraging attribute-level representations to evaluate outfit compatibility and quantify affinity between fashion items. Extensive experiments demonstrate that AFRec achieves state-of-the-art recommendation accuracy and generates intuitive explanations simultaneously.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Mingxin Gan, Yingxue Ma
Summary: Representation learning and deep learning have been applied in recommendation systems. This paper proposes a deep transfer learning-based recommendation model that generates personalized user representations by transferring knowledge from multiple user activities. Experimental results show that the proposed model outperforms existing methods in recommendation performance, convergence, and robustness.
Article
Computer Science, Artificial Intelligence
Tongcun Liu, Siyuan Lou, Jianxin Liao, Hailin Feng
Summary: The proposed dynamic and static representation learning network (DSRLN) aims to improve rating prediction accuracy by exploring fine-grained representations of users and items. By utilizing a dynamic representation extractor and a static representation extractor, the network models the dynamic evolution of users' interests and intrinsic preferences, respectively, with a personalized adaptive fusion module designed to identify the different influences of dynamic and static features for different users.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Aminu Da'u, Naomie Salim, Rabiu Idris
Summary: This paper proposes a RS model that utilizes neural attention techniques to learn adaptive user/item representations and fine-grained user-item interactions, aiming to enhance the accuracy of item recommendation. Experimental results show that the proposed model outperforms existing methods in terms of rating prediction and ranking performances.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)